Systems and methods for reducing LiDAR points

ABSTRACT

In one embodiment, a system for removing LiDAR points is provided. An image is received from a camera and a plurality of points is received from a LiDAR sensor. The points are placed on the image based on coordinates associated with each point. The image is divided into a plurality of cells by placing a grid over the image. For each cell, a threshold is calculated based on the minimum distance between the points in the cell and the camera. The threshold may control how many points are allowed to remain in each cell. After calculating the thresholds, points are removed from each cell until the number of points in each cell do not exceed the threshold for the cell. The image and/or the reduced plurality of points are then used to provide one or more vehicle functions.

TECHNICAL FIELD

The subject matter described herein relates, in general, to systems andmethods for reducing LiDAR points, and, in particular, to using a gridand distances from a camera to reduce LiDAR points in an image.

BACKGROUND

LiDAR sensors are popular sensors for use in autonomous andsemi-autonomous vehicles. A LiDAR sensor measures the distance to atarget by illuminating the target with a pulse laser, and measuring thereflected pulses. Differences in return times and wavelengths for thepulses can then be used to generate a 3D representation of the target.Often, LiDAR sensors include a plurality of lasers, with each laseroutputting a pulse laser at a different angle.

While LiDAR sensors are very sensitive and can provide a very accuraterepresentation of a target, there are drawbacks associated with LiDARsensors. One drawback in the sheer amount of data (called points)generated by the LiDAR sensor. Each point may have an associateddistance from the LiDAR sensor. LiDAR points are often used for avariety of post-processing tasks such as shape recognition, directiondetection, and shape-matching with previous frames. When the number ofsuch points are large, such post-processing tasks can be become boggeddown and may take a long time to execute. Because such tasks are timesensitive, there is a need to reduce the number of points that areprovided by LiDAR sensors without compromising the overall effectivenessof the LiDAR sensors.

SUMMARY

In one embodiment, a system for removing LiDAR points is provided. Animage is received from a camera and a plurality of points is receivedfrom a LiDAR sensor. The points are placed on the image based oncoordinates associated with each point. The image is divided into aplurality of cells by placing a grid over the image. For each cell, athreshold is calculated based on the minimum distance between the pointsin the cell and the camera. The threshold may control how many pointsare allowed to remain in each cell. After calculating the thresholds,points are removed from each cell until the number of points in eachcell do not exceed the threshold for the cell. The image and/or thereduced plurality of points are then used to provide one or more vehiclefunctions.

As will be described further below, the system solves may of theproblems associated with large numbers of LiDAR points. First, byremoving LiDAR points from cells, the overall number of LiDAR points isreduced leading to faster processing and resource usage by vehiclefunctions that rely on the LiDAR points. Second, because the thresholdfor each cell is based on the distance between the points in the celland the camera, cells that are closer to the camera will have morepoints removed than cells that are farther from the camera. BecauseLiDAR points tend to be denser at a closer distance, removing pointsfrom close cells may have less effect on the vehicle functions that usethe points.

In an embodiment, a system for reducing LiDAR points for a vehicle isprovided. The system includes one or more processors and a memorycommunicably coupled to the one or more processors. The system furtherincludes a reduction module including instructions that when executed bythe one or more processors cause the one or more processors to: receivean image from a camera associated with the vehicle; receive a pluralityof points from a LiDAR sensor associated with the vehicle, wherein eachpoint of the plurality of points is associated with a position in theimage; divide the image into a plurality of cells, wherein each point ofthe plurality of points is associated with a cell of the plurality ofcells based on the position of the point in the image; for each cell ofthe plurality of cells, calculate a threshold for the cell based on thepoints associated with cell; for each cell of the plurality of cells,delete points associated with the cell until a number of pointsassociated with the cell satisfies the threshold; and use the remainingpoints for one or more vehicle functions.

In an embodiment, a method for reducing LiDAR points is provided. Themethod includes: receiving an image from a camera associated with avehicle; receiving a plurality of points from a LiDAR sensor associatedwith the vehicle, wherein each point of the plurality of points isassociated with a position in the image; dividing the image into aplurality of cells, wherein each point of the plurality of points isassociated with a cell of the plurality of cells based on the positionof the point in the image; for each cell of the plurality of cells,calculating a threshold for the cell based on the points associated withcell; for each cell of the plurality of cells, deleting pointsassociated with the cell until a number of points associated with thecell satisfies the threshold; and using the remaining points and theimage for one or more vehicle functions.

In an embodiment, a non-transitory computer-readable medium for reducingLiDAR points is provided. The medium including instructions that whenexecuted by one or more processors cause the one or more processors to:receive an image from a camera associated with a vehicle; receive aplurality of points from a LiDAR sensor associated with the vehicle,wherein each point of the plurality of points is associated with aposition in the image; divide the image into a plurality of cells,wherein each point of the plurality of points is associated with a cellof the plurality of cells based on the position of the point in theimage; for each cell of the plurality of cells, calculate a thresholdfor the cell based on the points associated with cell; for each cell ofthe plurality of cells, delete points associated with the cell until anumber of points associated with the cell satisfies the threshold; anduse the remaining points for one or more vehicle functions.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various systems, methods, andother embodiments of the disclosure. It will be appreciated that theillustrated element boundaries (e.g., boxes, groups of boxes, or othershapes) in the figures represent one embodiment of the boundaries. Insome embodiments, one element may be designed as multiple elements ormultiple elements may be designed as one element. In some embodiments,an element shown as an internal component of another element may beimplemented as an external component and vice versa. Furthermore,elements may not be drawn to scale.

FIG. 1 illustrates one embodiment of a vehicle within which systems andmethods disclosed herein may be implemented.

FIG. 2 illustrates one embodiment of a point reduction system.

FIGS. 3-5 illustrate example images.

FIG. 4 illustrates a flowchart of a method that is associated withreducing the number of points in an image.

FIG. 5 illustrates a flowchart of a method that is associated withcalculating a threshold for a cell of a grid.

FIG. 6 illustrates a flowchart of a method that is associated withremoving points from an image.

FIG. 7 illustrates a flowchart of a method that is associated withcalculating a threshold for cell of a grid.

DETAILED DESCRIPTION

With regards to FIG. 1, a vehicle 100 includes various elements. It willbe understood that in various embodiments it may not be necessary forthe vehicle 100 to have all of the elements shown in FIG. 1. The vehicle100 can have any combination of the various elements shown in FIG. 1.Further, the vehicle 100 can have additional elements to those shown inFIG. 1. In some arrangements, the vehicle 100 may be implemented withoutone or more of the elements shown in FIG. 1. While the various elementsare shown as being located within the vehicle 100 in FIG. 1, it will beunderstood that one or more of these elements can be located external tothe vehicle 100. Further, the elements shown may be physically separatedby large distances.

Some of the possible elements of the vehicle 100 are shown in FIG. 1 andwill be described along with subsequent figures. However, a descriptionof many of the elements in FIG. 1 will be provided after the discussionof FIGS. 2-7 for purposes of brevity of this description. Additionally,it will be appreciated that for simplicity and clarity of illustration,where appropriate, reference numerals have been repeated among thedifferent figures to indicate corresponding or analogous elements. Inaddition, the discussion outlines numerous specific details to provide athorough understanding of the embodiments described herein. Those ofskill in the art, however, will understand that the embodimentsdescribed herein may be practiced using various combinations of theseelements.

In either case, the vehicle 100 includes the point reduction system 170that is implemented to reduce the number of points provided by a LiDARsensor. The noted functions and methods will become more apparent with afurther discussion of the figures.

With reference to FIG. 2, one embodiment of the point reduction system170 of FIG. 1 is further illustrated. The point reduction system 170 isshown as including a processor 110 from the vehicle 100 of FIG. 1.Accordingly, the processor 110 may be a part of the point reductionsystem 170, the point reduction system 170 may include a separateprocessor from the processor 110 of the vehicle 100, or the pointreduction system 170 may access the processor 110 through a data bus oranother communication path. It should be appreciated, that while thepoint reduction system 170 is illustrated as being a single containedsystem, in various embodiments, the point reduction system 170 is adistributed system that is comprised of components that can be providedas a centralized server, a cloud-based service, and so on.

In one embodiment, the point reduction system 170 includes a memory 210that stores a reduction module 220. The memory 210 is a random-accessmemory (RAM), read-only memory (ROM), a hard-disk drive, a flash memory,or other suitable memory for storing the module 220. The module 220 is,for example, computer-readable instructions that when executed by theprocessor 110 cause the processor 110 to perform the various functionsdisclosed herein. Moreover, as previously noted, in various embodiments,one or more aspects of the point reduction system 170 are implemented ascloud-based services, and so on. Thus, one or more modules of the pointreduction system 170 may be located remotely from other components andmay be implemented in a distributed manner.

Furthermore, in one embodiment, the point reduction system 170 includesthe database 240. The database 240 is, in one embodiment, an electronicdata structure stored in the memory 210 or another data store and thatis configured with routines that can be executed by the processor 110for analyzing stored data, providing stored data, organizing storeddata, and so on. Thus, in one embodiment, the database 240 stores dataused by the module 220 in executing various functions. In oneembodiment, the database 240 includes an image 280 along with, forexample, other information that is used and/or generated by the module220 such as points 285, a grid 287, and a threshold 293. Of course, infurther embodiments, the various information may be stored within thememory 210 or another suitable location.

The reduction module 220 is configured to receive a plurality of points285, and to reduce the number of points 285 in the plurality of points285 with compromising the accuracy or effectiveness of the points 285with respect to one or more vehicle functions that may rely on thepoints 285. Depending on the embodiment, these vehicle functions mayinclude functions such as shape recognition, direction detection, andshape-matching. The functions may further include autonomous vehiclerelated functions performed by the autonomous driving modules 160 of thevehicle 100.

The points 285 may be received by the reduction module 220 from one ormore LiDAR sensors 124 associated with the vehicle 100. Each point 285may be associated with coordinates (i.e., x and y coordinates) and adistance from the particular LiDAR sensor 124 that provided the point285. The points 285 may represent an area around the vehicle 100. Anysystem or method for generating points 285 from a LiDAR sensor 124 maybe used.

The reduction module 220 is further configured to receive an image 280.The reduction module 220 may receive the image 280 from one or morecameras 126 associated with the vehicle 100. The image 280 may be animage of an area around the vehicle 100. The image 280 may be of thesame general area as represented by the points 285. Any type of image280 may be supported.

The reduction module 220 may be configured to project or place thepoints 285 onto the image 280. Depending on the embodiment, thereduction module 220 may place each point 285 onto the image 280 basedon the coordinates associated with each point. Any method may be used.

The reduction module 220 may be configured to divide the image 280 intoa plurality of cells using a grid 287. The size of the cells of the grid287 may be based on properties of the LiDAR sensor 124 associated withthe vehicle 100. As may be appreciated, a LiDAR sensor 124 may include aplurality of lasers that each rotate around at a particular angle. Eachlaser may be configured to generate a specified number of laser pulsesper rotation (each laser pulse may result in one point 285). Each cellin the grid 287 may be sized such that it contains at most one point 285generated from a particular laser of the LiDAR sensor 124 for a givenrotation. Depending on the embodiment, each cell may be approximatelythe same size, or may have different sizes. The size of each cell may beset by a user or administrator.

As may be appreciated, each cell of the grid 287 may capture or includea plurality of points 285 that have been projected onto the image 280.Each point 280 may be included in one cell of the grid 287, and eachcell may include multiple points 285.

The reduction module 220 may be configured to calculate a threshold 293for each cell of the grid 298. The threshold 293 calculated by thereduction module 220 for a cell of the grid 287 may be a desired numberof points 285 to be included the cell. The reduction module 220 mayremove points 285 from a cell such that the number of points 285 in thecell do not exceed the threshold 293. The reduction module 220 maycalculate the thresholds 293 for the cell of the grid 287 such thatcells that include points 285 that are farther from the camera 126 onthe vehicle 100 have less points 285 removed than cells that includepoints 285 that are close to the camera 126. In general, points 285 aredenser in the image 280 closer to the camera 126 (or LiDAR 124), andless dense in the image 280 farther from the camera 126. Accordingly, byremoving more overall points 285 from close cells, the total number ofpoints 285 can be reduced with compromising the effectiveness of theremaining points 285 with respect to one or more vehicle functions.

In some implementations, the reduction module 220 may calculate athreshold 293 for a cell by calculating the distance of each point 285in the cell to the camera 126. The distance for a point 285 may becalculated based on its associated position in the image 180 and/or itsassociated distance from the LiDAR 124. Any method for calculatingdistance may be used.

After calculation each distance, the reduction module 220 may determinethe minimum distance among all of the points 285 in the cell. Thereduction module 220 may then determine the threshold 293 for the cellbased on the minimum distance. Generally, the higher the minimumdistance, the higher the threshold 293. The particular function used tocalculate or determine the threshold 293 for a cell may be set by a useror administrator.

In other implementations, rather than determine the threshold 293 basedon the minimum distance, the reduction module 220 may calculate theaverage distance among some or all of the points in the cell, and maycalculate the threshold 293 using the average distance. In still otherimplementations, the reduction module 220 may select a random point 285from the cell, and may use the distance of the random point 285 tocalculate the threshold 293 for the cell.

The reduction module 220 may, for each cell, remove points 285 from thecell until the threshold 293 calculated for the cell is satisfied. Thethreshold 293 for a cell may be satisfied when the total number ofpoints 285 in the cell are less than or equal to the threshold 293.Depending on the embodiment, the reduction module 220 may randomlyselect points 285 to remove. Alternatively, the reduction module 220 mayselect points 285 to remove using a criteria such as favoring theremoval of points 285 that are closer to other points 285 in the cell,or favoring the removal of points 285 with lower distances to the camera126. Other criteria may be used. The criteria may be set by a user oradministrator.

After removing some of the points 285, the reduction module 220 may usethe image 280 and/or the remaining points 285 to perform one or morevehicle function including shape recognition, direction detection, andshape-matching. Other vehicle functions may be supported. Depending onthe embodiment, the reduction module 220 may provide the image 280 andpoints 285 for use by one or more autonomous driving modules 160.

As may be appreciated, the point reduction system 170 described hereinsolves many of the problems noted above with respect to the use ofpoints 285 from LiDAR sensors 124, especially in combination with images280 received from cameras 126. First, by reducing the total number ofpoints 285, the remaining points 285 may be used to provide vehiclefunctions while requiring less processing resources. Second, by favoringthe removal of points 285 that are closer to the camera 126 over thosethat are farther away from the camera 126, the point reduction system170 removes points 285 that are likely to be duplicative of other points285.

FIG. 3 is an illustration of an example image 300. As shown, the image300 is a picture of vehicles taken from a camera 126 of a vehicle 100.For purposes of illustration only, bounding boxes generated by acomputer vision algorithm are shown surrounding each vehicle in theimage 300. Also shown in the image 300 are a plurality of points 285provided by a LiDAR sensor 124. The points 285 have been projected intothe image 300 and are illustrated in the image 300 as black dots.

Continuing to FIG. 4, as shown a grid 405 has been overlaid on the image300. The grid 405 includes a plurality of cells and each cell includessubset of the plurality of points 285. As can be seen in the image 300,the cells of the grid 405 that are closer to the camera 126 (i.e., thecells at the bottom of the image 300) generally include more points 285than the cells of the grid 405 that are farther from the camera 126(i.e., the cells at the top of the image 300).

As described above, the point reduction system 170 may calculate athreshold 293 for each of the cell of the grid 405 based in part on howclose the points 285 in the cell are to the camera 126. The threshold293 may be an upper bound on the number of points 285 that may beincluded in the cell. Therefore, continuing the example above, thethresholds 293 calculated for cells in the lower areas of the image 300(i.e., the cells with points 285 that are closer to the camera 126) maybe lower than the thresholds 293 calculated for cells in the higherareas of the image 300 (i.e., the cells with points 285 that are closerto the camera 126). The point reduction system 170 may remove points 285from each cell until the number of points 285 remaining in each celldoes not exceed the threshold 293 calculated for the cell.

FIG. 5 is an illustration of the image 300 after the point reductionsystem 170 removed some portion of the points 285. As can be seen, theimage 280 includes overall less points 285, and more points 285 havebeen removed from cells of the image 280 that are closer to the camera126 than the cells of the image 280 that are farther from the camera126.

Additional aspects of point removal will be discussed in relation toFIG. 6. FIG. 6 illustrates a flowchart of a method 600 that isassociated with removing points 285 from an image 280. The method 600will be discussed from the perspective of the point reduction system 170of FIGS. 1 and 2. While the method 600 is discussed in combination withthe point reduction system 170, it should be appreciated that the method600 is not limited to being implemented within the point reductionsystem 170 but is instead one example of a system that may implement themethod 600.

At 610, the reduction module 220 receives a plurality of points 285 froma LiDAR sensor 124. Each point may be associated with a position (e.g.,x and y coordinates) and a distance. The points 285 may represent ascene or environment of a vehicle 100.

At 620, the reduction module 220 receives an image 280 from a camera126. The image 280 may represent the same scene or environment as theplurality of points 285. The reduction module 220 may further merge theimage 280 and points 285 by placing or projection each of the points 285into the image 280 based on the positions associated with each point285.

At 630, the reduction module 220 divides the image 280 into a pluralityof cells. The reduction module 220 may divide the image 280 into aplurality of cells using a grid 287. Each cell of the grid may includenone or some of the points 285. Each point 285 be includes in at mostone cell. The size of each cell may be based on properties of the LiDARsensor 124 such as resolution.

At 640, the reduction module 220, for each cell, calculates a threshold293 for the cell. The threshold 293 for a cell may control how manypoints 285 may be contained in or associated with the cell. In someimplementation, the closer the points 285 in the cell to the camera 126,the lower the threshold 293. The threshold 293 may be based in part onthe minimum distance to the camera 126 from a point 285 in the cell.Other methods for calculating the threshold 293 may be used.

At 650, the reduction module 220, for each cell, deletes points from thecell until a total number of points in the cell satisfy the threshold293. Depending on the embodiment, the reduction module 220 may randomlyselect points 285 to be deleted, or may use some criteria ordeterministic method to select points 285 to delete. Any method may beused.

At 660, the reduction module 220 uses the remaining points 285 as inputto one or more vehicle functions. The remaining points 285 may be usedalong with the image 280. The vehicle functions may include shaperecognition, direction detection, and shape-matching. Other vehiclefunctions may be supported.

Additional aspects of vehicle safety will be discussed in relation toFIG. 7. FIG. 7 illustrates a flowchart of a method 700 that isassociated with calculating a threshold for cell of a grid 287. Themethod 700 will be discussed from the perspective of the point reductionsystem 170 of FIGS. 1 and 2. While the method 700 is discussed incombination with the point reduction system 170, it should beappreciated that the method 700 is not limited to being implementedwithin the point reduction system 170 but is instead one example of asystem that may implement the method 700.

At 710, the reduction module 220, for each point 285 in a cell,calculates a distance from the point 285 to the camera 126 associatedwith the vehicle 100. The distance from the point 285 to the camera maybe calculated based on the position of the point 285 in the image 280,and/or the distance associated with the point 285 to the LiDAR sensor124. Any method for calculating distance of points 285 may be used.

At 720, the reduction module 220 determines a minimum distance for thepoints 285 associated with the cell. The reduction module 220 mayinspect the distances calculated at 710, and may select the minimumdistance from among the calculated distances.

At 730, the reduction module 220 calculates the threshold 293 for thecell based on the minimum distance. The reduction module 220 maycalculate the threshold 293 for the cell such that a cell with a lowminimum distance receives a lower threshold 293 than a cell with a highminimum distance. The threshold 293 may be proportional to thecalculated minimum distance. Any method for calculating a threshold 293may be used.

FIG. 1 will now be discussed in full detail as an example environmentwithin which the system and methods disclosed herein may operate. Insome instances, the vehicle 100 is configured to switch selectivelybetween an autonomous mode, one or more semi-autonomous operationalmodes, and/or a manual mode. Such switching can be implemented in asuitable manner, now known or later developed. “Manual mode” means thatall of or a majority of the navigation and/or maneuvering of the vehicleis performed according to inputs received from a user (e.g., humandriver). In one or more arrangements, the vehicle 100 can be aconventional vehicle that is configured to operate in only a manualmode.

In one or more embodiments, the vehicle 100 is an autonomous vehicle. Asused herein, “autonomous vehicle” refers to a vehicle that operates inan autonomous mode. “Autonomous mode” refers to navigating and/ormaneuvering the vehicle 100 along a travel route using one or morecomputing systems to control the vehicle 100 with minimal or no inputfrom a human driver. In one or more embodiments, the vehicle 100 ishighly automated or completely automated. In one embodiment, the vehicle100 is configured with one or more semi-autonomous operational modes inwhich one or more computing systems perform a portion of the navigationand/or maneuvering of the vehicle along a travel route, and a vehicleoperator (i.e., driver) provides inputs to the vehicle to perform aportion of the navigation and/or maneuvering of the vehicle 100 along atravel route.

The vehicle 100 can include one or more processors 110. In one or morearrangements, the processor(s) 110 can be a main processor of thevehicle 100. For instance, the processor(s) 110 can be an electroniccontrol unit (ECU). The vehicle 100 can include one or more data stores115 for storing one or more types of data. The data store 115 caninclude volatile and/or non-volatile memory. Examples of suitable datastores 115 include RAM (Random Access Memory), flash memory, ROM (ReadOnly Memory), PROM (Programmable Read-Only Memory), EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), registers, magnetic disks, opticaldisks, hard drives, or any other suitable storage medium, or anycombination thereof. The data store 115 can be a component of theprocessor(s) 110, or the data store 115 can be operatively connected tothe processor(s) 110 for use thereby. The term “operatively connected,”as used throughout this description, can include direct or indirectconnections, including connections without direct physical contact.

In one or more arrangements, the one or more data stores 115 can includemap data 116. The map data 116 can include maps of one or moregeographic areas. In some instances, the map data 116 can includeinformation or data on roads, traffic control devices, road markings,structures, features, and/or landmarks in the one or more geographicareas. The map data 116 can be in any suitable form. In some instances,the map data 116 can include aerial views of an area. In some instances,the map data 116 can include ground views of an area, including360-degree ground views. The map data 116 can include measurements,dimensions, distances, and/or information for one or more items includedin the map data 116 and/or relative to other items included in the mapdata 116. The map data 116 can include a digital map with informationabout road geometry. The map data 116 can be high quality and/or highlydetailed.

In one or more arrangements, the map data 116 can include one or moreterrain maps 117. The terrain map(s) 117 can include information aboutthe ground, terrain, roads, surfaces, and/or other features of one ormore geographic areas. The terrain map(s) 117 can include elevation datain the one or more geographic areas. The map data 116 can be highquality and/or highly detailed. The terrain map(s) 117 can define one ormore ground surfaces, which can include paved roads, unpaved roads,land, and other things that define a ground surface.

In one or more arrangements, the map data 116 can include one or morestatic obstacle maps 118. The static obstacle map(s) 118 can includeinformation about one or more static obstacles located within one ormore geographic areas. A “static obstacle” is a physical object whoseposition does not change or substantially change over a period of timeand/or whose size does not change or substantially change over a periodof time. Examples of static obstacles include trees, buildings, curbs,fences, railings, medians, utility poles, statues, monuments, signs,benches, furniture, mailboxes, large rocks, hills. The static obstaclescan be objects that extend above ground level. The one or more staticobstacles included in the static obstacle map(s) 118 can have locationdata, size data, dimension data, material data, and/or other dataassociated with it. The static obstacle map(s) 118 can includemeasurements, dimensions, distances, and/or information for one or morestatic obstacles. The static obstacle map(s) 118 can be high qualityand/or highly detailed. The static obstacle map(s) 118 can be updated toreflect changes within a mapped area.

The one or more data stores 115 can include sensor data 119. In thiscontext, “sensor data” means any information about the sensors that thevehicle 100 is equipped with, including the capabilities and otherinformation about such sensors. As will be explained below, the vehicle100 can include the sensor system 120. The sensor data 119 can relate toone or more sensors of the sensor system 120. As an example, in one ormore arrangements, the sensor data 119 can include information on one ormore LiDAR sensors 124 of the sensor system 120.

In some instances, at least a portion of the map data 116 and/or thesensor data 119 can be located in one or more data stores 115 locatedonboard the vehicle 100. Alternatively, or in addition, at least aportion of the map data 116 and/or the sensor data 119 can be located inone or more data stores 115 that are located remotely from the vehicle100.

As noted above, the vehicle 100 can include the sensor system 120. Thesensor system 120 can include one or more sensors. “Sensor” means anydevice, component and/or system that can detect, and/or sense something.The one or more sensors can be configured to detect, and/or sense inreal-time. As used herein, the term “real-time” means a level ofprocessing responsiveness that a user or system senses as sufficientlyimmediate for a particular process or determination to be made, or thatenables the processor to keep up with some external process.

In arrangements in which the sensor system 120 includes a plurality ofsensors, the sensors can work independently from each other.Alternatively, two or more of the sensors can work in combination witheach other. In such case, the two or more sensors can form a sensornetwork. The sensor system 120 and/or the one or more sensors can beoperatively connected to the processor(s) 110, the data store(s) 115,and/or another element of the vehicle 100 (including any of the elementsshown in FIG. 1). The sensor system 120 can acquire data of at least aportion of the external environment of the vehicle 100 (e.g., nearbyvehicles).

The sensor system 120 can include any suitable type of sensor. Variousexamples of different types of sensors will be described herein.However, it will be understood that the embodiments are not limited tothe particular sensors described. The sensor system 120 can include oneor more vehicle sensors 121. The vehicle sensor(s) 121 can detect,determine, and/or sense information about the vehicle 100 itself. In oneor more arrangements, the vehicle sensor(s) 121 can be configured todetect, and/or sense position and orientation changes of the vehicle100, such as, for example, based on inertial acceleration. In one ormore arrangements, the vehicle sensor(s) 121 can include one or moreaccelerometers, one or more gyroscopes, an inertial measurement unit(IMU), a dead-reckoning system, a global navigation satellite system(GNSS), a global positioning system (GPS), a navigation system 147,and/or other suitable sensors. The vehicle sensor(s) 121 can beconfigured to detect, and/or sense one or more characteristics of thevehicle 100. In one or more arrangements, the vehicle sensor(s) 121 caninclude a speedometer to determine a current speed of the vehicle 100.

Alternatively, or in addition, the sensor system 120 can include one ormore environment sensors 122 configured to acquire, and/or sense drivingenvironment data. “Driving environment data” includes data orinformation about the external environment in which an autonomousvehicle is located or one or more portions thereof. For example, the oneor more environment sensors 122 can be configured to detect, quantifyand/or sense obstacles in at least a portion of the external environmentof the vehicle 100 and/or information/data about such obstacles. Suchobstacles may be stationary objects and/or dynamic objects. The one ormore environment sensors 122 can be configured to detect, measure,quantify and/or sense other things in the external environment of thevehicle 100, such as, for example, lane markers, signs, traffic lights,traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100,off-road objects, etc.

Various examples of sensors of the sensor system 120 will be describedherein. The example sensors may be part of the one or more environmentsensors 122 and/or the one or more vehicle sensors 121. However, it willbe understood that the embodiments are not limited to the particularsensors described.

As an example, in one or more arrangements, the sensor system 120 caninclude one or more radar sensors 123, one or more LIDAR sensors 124,one or more sonar sensors 125, and/or one or more cameras 126. In one ormore arrangements, the one or more cameras 126 can be high dynamic range(HDR) cameras or infrared (IR) cameras.

The vehicle 100 can include an input system 130. An “input system”includes any device, component, system, element or arrangement or groupsthereof that enable information/data to be entered into a machine. Theinput system 130 can receive an input from a vehicle passenger (e.g., adriver or a passenger). The vehicle 100 can include an output system135. An “output system” includes any device, component, or arrangementor groups thereof that enable information/data to be presented to avehicle passenger (e.g., a person, a vehicle passenger, etc.).

The vehicle 100 can include one or more vehicle systems 140. Variousexamples of the one or more vehicle systems 140 are shown in FIG. 1.However, the vehicle 100 can include more, fewer, or different vehiclesystems. It should be appreciated that although particular vehiclesystems are separately defined, each or any of the systems or portionsthereof may be otherwise combined or segregated via hardware and/orsoftware within the vehicle 100. The vehicle 100 can include apropulsion system 141, a braking system 142, a steering system 143,throttle system 144, a transmission system 145, a signaling system 146,and/or a navigation system 147. Each of these systems can include one ormore devices, components, and/or a combination thereof, now known orlater developed.

The navigation system 147 can include one or more devices, applications,and/or combinations thereof, now known or later developed, configured todetermine the geographic location of the vehicle 100 and/or to determinea travel route for the vehicle 100. The navigation system 147 caninclude one or more mapping applications to determine a travel route forthe vehicle 100. The navigation system 147 can include a globalpositioning system, a local positioning system or a geolocation system.

The processor(s) 110, the point reduction system 170, and/or theautonomous driving module(s) 160 can be operatively connected tocommunicate with the various vehicle systems 140 and/or individualcomponents thereof. For example, returning to FIG. 1, the processor(s)110 and/or the autonomous driving module(s) 160 can be in communicationto send and/or receive information from the various vehicle systems 140to control the movement, speed, maneuvering, heading, direction, etc. ofthe vehicle 100. The processor(s) 110, the point reduction system 170,and/or the autonomous driving module(s) 160 may control some or all ofthese vehicle systems 140 and, thus, may be partially or fullyautonomous.

The processor(s) 110, the point reduction system 170, and/or theautonomous driving module(s) 160 can be operatively connected tocommunicate with the various vehicle systems 140 and/or individualcomponents thereof. For example, returning to FIG. 1, the processor(s)110, the point reduction system 170, and/or the autonomous drivingmodule(s) 160 can be in communication to send and/or receive informationfrom the various vehicle systems 140 to control the movement, speed,maneuvering, heading, direction, etc. of the vehicle 100. Theprocessor(s) 110, the point reduction system 170, and/or the autonomousdriving module(s) 160 may control some or all of these vehicle systems140.

The processor(s) 110, the point reduction system 170, and/or theautonomous driving module(s) 160 may be operable to control thenavigation and/or maneuvering of the vehicle 100 by controlling one ormore of the vehicle systems 140 and/or components thereof. For instance,when operating in an autonomous mode, the processor(s) 110, the pointreduction system 170, and/or the autonomous driving module(s) 160 cancontrol the direction and/or speed of the vehicle 100. The processor(s)110, the point reduction system 170, and/or the autonomous drivingmodule(s) 160 can cause the vehicle 100 to accelerate (e.g., byincreasing the supply of fuel provided to the engine), decelerate (e.g.,by decreasing the supply of fuel to the engine and/or by applyingbrakes) and/or change direction (e.g., by turning the front two wheels).As used herein, “cause” or “causing” means to make, force, compel,direct, command, instruct, and/or enable an event or action to occur orat least be in a state where such event or action may occur, either in adirect or indirect manner.

The vehicle 100 can include one or more actuators 150. The actuators 150can be any element or combination of elements operable to modify, adjustand/or alter one or more of the vehicle systems 140 or componentsthereof to responsive to receiving signals or other inputs from theprocessor(s) 110 and/or the autonomous driving module(s) 160. Anysuitable actuator can be used. For instance, the one or more actuators150 can include motors, pneumatic actuators, hydraulic pistons, relays,solenoids, and/or piezoelectric actuators, just to name a fewpossibilities.

The vehicle 100 can include one or more modules, at least some of whichare described herein. The modules can be implemented ascomputer-readable program code that, when executed by a processor 110,implement one or more of the various processes described herein. One ormore of the modules can be a component of the processor(s) 110, or oneor more of the modules can be executed on and/or distributed among otherprocessing systems to which the processor(s) 110 is operativelyconnected. The modules can include instructions (e.g., program logic)executable by one or more processor(s) 110. Alternatively, or inaddition, one or more data store 115 may contain such instructions.

In one or more arrangements, one or more of the modules described hereincan include artificial or computational intelligence elements, e.g.,neural network, fuzzy logic or other machine learning algorithms.Further, in one or more arrangements, one or more of the modules can bedistributed among a plurality of the modules described herein. In one ormore arrangements, two or more of the modules described herein can becombined into a single module.

The vehicle 100 can include one or more autonomous driving modules 160.The autonomous driving module(s) 160 can be configured to receive datafrom the sensor system 120 and/or any other type of system capable ofcapturing information relating to the vehicle 100 and/or the externalenvironment of the vehicle 100. In one or more arrangements, theautonomous driving module(s) 160 can use such data to generate one ormore driving scene models. The autonomous driving module(s) 160 candetermine position and velocity of the vehicle 100. The autonomousdriving module(s) 160 can determine the location of obstacles,obstacles, or other environmental features including traffic signs,trees, shrubs, neighboring vehicles, pedestrians, etc.

The autonomous driving module(s) 160 can be configured to receive,and/or determine location information for obstacles within the externalenvironment of the vehicle 100 for use by the processor(s) 110, and/orone or more of the modules described herein to estimate position andorientation of the vehicle 100, vehicle position in global coordinatesbased on signals from a plurality of satellites, or any other dataand/or signals that could be used to determine the current state of thevehicle 100 or determine the position of the vehicle 100 with respect toits environment for use in either creating a map or determining theposition of the vehicle 100 in respect to map data.

The autonomous driving module(s) 160 either independently or incombination with the point reduction system 170 can be configured todetermine travel path(s), current autonomous driving maneuvers for thevehicle 100, future autonomous driving maneuvers and/or modifications tocurrent autonomous driving maneuvers based on data acquired by thesensor system 120, driving scene models, and/or data from any othersuitable source such as determinations from the sensor data 250.“Driving maneuver” means one or more actions that affect the movement ofa vehicle. Examples of driving maneuvers include: accelerating,decelerating, braking, turning, moving in a lateral direction of thevehicle 100, changing travel lanes, merging into a travel lane, and/orreversing, just to name a few possibilities. The autonomous drivingmodule(s) 160 can be configured can be configured to implementdetermined driving maneuvers. The autonomous driving module(s) 160 cancause, directly or indirectly, such autonomous driving maneuvers to beimplemented. As used herein, “cause” or “causing” means to make,command, instruct, and/or enable an event or action to occur or at leastbe in a state where such event or action may occur, either in a director indirect manner. The autonomous driving module(s) 160 can beconfigured to execute various vehicle functions and/or to transmit datato, receive data from, interact with, and/or control the vehicle 100 orone or more systems thereof (e.g., one or more of vehicle systems 140).

Detailed embodiments are disclosed herein. However, it is to beunderstood that the disclosed embodiments are intended only as examples.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative basis for teaching one skilled in the artto variously employ the aspects herein in virtually any appropriatelydetailed structure. Further, the terms and phrases used herein are notintended to be limiting but rather to provide an understandabledescription of possible implementations. Various embodiments are shownin FIGS. 1-7, but the embodiments are not limited to the illustratedstructure or application.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flowcharts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved.

The systems, components and/or processes described above can be realizedin hardware or a combination of hardware and software and can berealized in a centralized fashion in one processing system or in adistributed fashion where different elements are spread across severalinterconnected processing systems. Any kind of processing system oranother apparatus adapted for carrying out the methods described hereinis suited. A typical combination of hardware and software can be aprocessing system with computer-usable program code that, when beingloaded and executed, controls the processing system such that it carriesout the methods described herein. The systems, components and/orprocesses also can be embedded in a computer-readable storage, such as acomputer program product or other data programs storage device, readableby a machine, tangibly embodying a program of instructions executable bythe machine to perform methods and processes described herein. Theseelements also can be embedded in an application product which comprisesall the features enabling the implementation of the methods describedherein and, which when loaded in a processing system, is able to carryout these methods.

Furthermore, arrangements described herein may take the form of acomputer program product embodied in one or more computer-readable mediahaving computer-readable program code embodied, e.g., stored, thereon.Any combination of one or more computer-readable media may be utilized.The computer-readable medium may be a computer-readable signal medium ora computer-readable storage medium. The phrase “computer-readablestorage medium” means a non-transitory storage medium. Acomputer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: a portablecomputer diskette, a hard disk drive (HDD), a solid-state drive (SSD), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), adigital versatile disc (DVD), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer-readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

Generally, modules as used herein include routines, programs, objects,components, data structures, and so on that perform particular tasks orimplement particular data types. In further aspects, a memory generallystores the noted modules. The memory associated with a module may be abuffer or cache embedded within a processor, a RAM, a ROM, a flashmemory, or another suitable electronic storage medium. In still furtheraspects, a module as envisioned by the present disclosure is implementedas an application-specific integrated circuit (ASIC), a hardwarecomponent of a system on a chip (SoC), as a programmable logic array(PLA), or as another suitable hardware component that is embedded with adefined configuration set (e.g., instructions) for performing thedisclosed functions.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber, cable, RF, etc., or any suitable combination ofthe foregoing. Computer program code for carrying out operations foraspects of the present arrangements may be written in any combination ofone or more programming languages, including an object-orientedprogramming language such as Java™ Smalltalk, C++ or the like andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer, or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

The terms “a” and “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e., open language). The phrase “at leastone of . . . and . . . ” as used herein refers to and encompasses anyand all possible combinations of one or more of the associated listeditems. As an example, the phrase “at least one of A, B, and C” includesA only, B only, C only, or any combination thereof (e.g., AB, AC, BC orABC).

Aspects herein can be embodied in other forms without departing from thespirit or essential attributes thereof. Accordingly, reference should bemade to the following claims, rather than to the foregoingspecification, as indicating the scope hereof.

What is claimed is:
 1. A system for reducing LiDAR points for a vehiclecomprising: one or more processors; a memory communicably coupled to theone or more processors and storing: a reduction module includinginstructions that when executed by the one or more processors cause theone or more processors to: receive an image from a camera associatedwith the vehicle; receive a plurality of points from a LiDAR sensorassociated with the vehicle, wherein each point of the plurality ofpoints is associated with a position in the image; divide the image intoa plurality of cells, wherein each point of the plurality of points isassociated with a cell of the plurality of cells based on the positionof the point in the image; for each cell of the plurality of cells,calculate a threshold for the cell based on the points associated withcell; for each cell of the plurality of cells, delete points associatedwith the cell until a number of points associated with the cellsatisfies the threshold; and use the remaining points for one or morevehicle functions.
 2. The system of claim 1, wherein the one or morevehicle functions include shape recognition, direction detection, andshape-matching.
 3. The system of claim 1, wherein deleting pointsassociated with the cell until the number of points associated with thecell satisfies the threshold comprises randomly deleting points.
 4. Thesystem of claim 1, wherein the reduction module including instructionsthat when executed by the one or more processors cause the one or moreprocessors to calculate the threshold for the cell based on the pointsassociated with cell comprises the reduction module further includinginstructions that when executed by the one or more processors cause theone or more processors to: for each point associated with the cell,calculate a distance from the point to the camera based in part on theposition associated with the point; determine a minimum distance for thepoints associated with the cell; and calculate the threshold based onthe minimum distance.
 5. The system of claim 1, wherein each point isfurther associated with a distance to the LiDAR sensor, and wherein thereduction module including instructions that when executed by the one ormore processors cause the one or more processors to calculate thethreshold for the cell based on the points associated with cellcomprises the reduction module further including instructions that whenexecuted by the one or more processors cause the one or more processorsto: for each point associated with the cell, calculate a distance fromthe point to the camera based in part on the position and the distanceto the LiDAR sensor associated with the point; determine a minimumdistance from the points to the camera for the points associated withthe cell; and calculate the threshold based on the minimum distance fromthe points to the camera.
 6. The system of claim 1, wherein thereduction module including instructions that when executed by the one ormore processors cause the one or more processors to delete pointsassociated with the cell until the number of points associated with thecell satisfies the threshold comprises the reduction module furtherincluding instructions that when executed by the one or more processorscause the one or more processors to delete points associated with thecell until the number of points associated with the cell is less than orequal to the threshold.
 7. The system of claim 1, wherein the thresholdis non-zero.
 8. A method for reducing LiDAR points, comprising:receiving an image from a camera associated with a vehicle; receiving aplurality of points from a LiDAR sensor associated with the vehicle,wherein each point of the plurality of points is associated with aposition in the image; dividing the image into a plurality of cells,wherein each point of the plurality of points is associated with a cellof the plurality of cells based on the position of the point in theimage; for each cell of the plurality of cells, calculating a thresholdfor the cell based on the points associated with cell; for each cell ofthe plurality of cells, deleting points associated with the cell until anumber of points associated with the cell satisfies the threshold; andusing the remaining points and the image for one or more vehiclefunctions.
 9. The method of claim 7, wherein the one or more vehiclefunctions include shape recognition, direction detection, andshape-matching.
 10. The method of claim 7, wherein deleting pointsassociated with the cell until the number of points associated with thecell satisfies the threshold comprises randomly deleting points.
 11. Themethod of claim 7, wherein calculating the threshold for the cell basedon the points associated with cell comprises: for each point associatedwith the cell, calculating a distance from the point to the camera basedin part on the position associated with the point; determining a minimumdistance for the points associated with the cell; and calculating thethreshold based on the minimum distance.
 12. The method of claim 7,wherein each point is further associated with a distance to the LiDARsensor, and where calculating the threshold for the cell based on thepoints associated with cell comprises: for each point associated withthe cell, calculating a distance from the point to the camera based inpart on the position and the distance to the LiDAR sensor associatedwith the point; determining a minimum distance from the points to thecamera for the points associated with the cell; and calculating thethreshold based on the minimum distance from the points to the camera.13. The method of claim 7, wherein deleting points associated with thecell until the number of points associated with the cell satisfies thethreshold comprises deleting points associated with the cell until thenumber of points associated with the cell is less than or equal to thethreshold.
 14. The method of claim 8, wherein the threshold is non-zero.15. A non-transitory computer-readable medium for reducing LiDAR pointsincluding instructions that when executed by one or more processorscause the one or more processors to: receive an image from a cameraassociated with a vehicle; receive a plurality of points from a LiDARsensor associated with the vehicle, wherein each point of the pluralityof points is associated with a position in the image; divide the imageinto a plurality of cells, wherein each point of the plurality of pointsis associated with a cell of the plurality of cells based on theposition of the point in the image; for each cell of the plurality ofcells, calculate a threshold for the cell based on the points associatedwith cell; for each cell of the plurality of cells, delete pointsassociated with the cell until a number of points associated with thecell satisfies the threshold; and use the remaining points for one ormore vehicle functions.
 16. The computer-readable medium of claim 15,wherein the one or more vehicle functions include shape recognition,direction detection, and shape-matching.
 17. The computer-readablemedium of claim 15, wherein deleting points associated with the celluntil the number of points associated with the cell satisfies thethreshold comprises randomly deleting points.
 18. The computer-readablemedium of claim 15, wherein calculating the threshold for the cell basedon the points associated with cell comprises: for each point associatedwith the cell, calculating a distance from the point to the camera basedin part on the position associated with the point; determining a minimumdistance for the points associated with the cell; and calculating thethreshold based on the minimum distance.
 19. The computer-readablemedium of claim 15, wherein each point is further associated with adistance to the LiDAR sensor, and where calculating the threshold forthe cell based on the points associated with cell comprises: for eachpoint associated with the cell, calculating a distance from the point tothe camera based in part on the position and the distance to the LiDARsensor associated with the point; determining a minimum distance fromthe points to the camera for the points associated with the cell; andcalculating the threshold based on the minimum distance from the pointsto the camera.
 20. The computer-readable medium of claim 15, wherein thethreshold is non-zero.